Since there are no datasets directly available for point cloud upsampling with normal inference, we create them artificially by downsampling point clouds from existing point cloud datasets.
We use the PU-Net [1]} dataset providing point cloud patches segmented from large meshes.
The dataset consists of 4000 patches containing 4096 points in each point cloud with corresponding normals.
We use non-uniform downsampling to extract 1024 points from each point cloud to feed them as input to the network.
To avoid overfitting, we apply on-the-fly data augmentation using random shifting, rotation, scaling, and adding random Gaussian noise.
<FIGURE>